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<body>
<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################

## load packages
library(dplyr)   # CRAN v1.0.5
library(tidyr)   # CRAN v1.1.3
library(ggplot2) # CRAN v3.3.3
library(Hmisc)   # CRAN v4.4-2
library(scales)  # CRAN v1.1.1
library(here)    # CRAN v1.0.1


here::here()
</code></pre>

<pre><code>## [1] &quot;/Users/smueller/Dropbox/papers/ireland_incumbency_gaa&quot;
</code></pre>

<pre><code class="r">## alternatively, set working directory manually
## setwd(&quot;...&quot;)


## import code for custom ggplot2 theme
source(&quot;function_theme_base.R&quot;)

## load ISSP data. The raw dataset with all variables can be 
## downloaded at https://doi.org/10.4232/1.10079 
## after a free registration 
## The raw dataset is called ZA4850_v2-0-0.dta
## 
## Using dat_issp &lt;- foreign::read.dta(&quot;ZA4850_v2-0-0.dta&quot;)
## you can import the full dataset
## Here, we use a subsetted dataset containing the relevant variables 
## for the descriptive plots 
## V5 = country; 
## V44 = which sport is most frequently watched
## V45 = which sport is a respondent&#39;s second most frequently watched sports
## V46 = degree of pride when country performs well in sports on the international stage

## load ISSP data
dat_issp_raw &lt;- readRDS(&quot;data_issp_2009_sports.rds&quot;)


## separate V5 into country abbreviation and country
## recode very proud to a binary numeric variable
## proud_very_proud_num measures whether a respondent is proud or very proud
## when the country performs well in sports on the international stage

dat_issp &lt;- dat_issp_raw %&gt;% 
    separate(V5, into = c(&quot;county_abbr&quot;, &quot;country&quot;), sep = &quot;-&quot;) %&gt;% 
    mutate(country = car::recode(country, &quot;&#39;Great Britain (UK)&#39;=&#39;United Kingdom&#39;&quot;)) %&gt;% 
    mutate(very_proud_num = ifelse(V46 == &quot;I am very proud&quot;, 
                                   1 , 0)) %&gt;% 
    mutate(proud_very_proud_num = ifelse(V46 == &quot;I am very proud&quot; | V46 == &quot;I am somewhat proud&quot;, 1, 0))


## bootstrap means and 95/90% confidence intervals for proud/very proud
set.seed(134)
dat_issp_sum_proud_very_95 &lt;- dat_issp %&gt;% 
    group_by(country) %&gt;% 
    do(data.frame(rbind(Hmisc::smean.cl.boot(.$proud_very_proud_num,
                                             conf.int = .95)))) 

set.seed(134)
dat_issp_sum_proud_very_90_ci &lt;- dat_issp %&gt;% 
    group_by(country) %&gt;% 
    do(data.frame(rbind(Hmisc::smean.cl.boot(.$proud_very_proud_num,
                                             conf.int = .90)))) %&gt;% 
    select(Upper_90 = Upper,
           Lower_90 = Lower,
           country)


## merge both datasets to plot 90% and 95% confidence intervals
dat_issp_sum_proud_very &lt;- dat_issp_sum_proud_very_95 %&gt;% 
    left_join(dat_issp_sum_proud_very_90_ci, by = &quot;country&quot;) %&gt;% 
    mutate(ire_dummy = ifelse(country == &quot;Ireland&quot;, TRUE, FALSE))


#                # create plot with the proportions and 95% bootstrapped CIs

## Figure A01 ----
ggplot(dat_issp_sum_proud_very, aes(x = reorder(country, Mean), y = Mean,
                                    colour = ire_dummy)) +
    geom_point(size = 3) +
    geom_linerange(aes(ymin = Lower,
                       ymax = Upper),
                   size = 0.5) +
    geom_linerange(aes(ymin = Lower_90,
                       ymax = Upper_90),
                   size = 1.3) +
    geom_text(aes(label = country,
                  y = Lower),
              hjust = 1, nudge_y = -0.01) +
    coord_flip() +
    scale_colour_manual(values = c(&quot;grey70&quot;, &quot;black&quot;)) +
    theme(legend.position = &quot;none&quot;) + 
    scale_y_continuous(labels = scales::percent_format(accuracy = 1),
                       limits = c(0.6, 1)) +
    labs(y = &quot;Respondents who are somewhat proud/very proud\nwhen country does well at an international sports competition&quot; , x = NULL) +
    theme(legend.position = &quot;none&quot;,
          axis.ticks.y = element_blank(),
          axis.text.y = element_blank())
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a01.pdf&quot;, 
       width = 9, 
       height = 6)


## now check how often selected sports in certain countries are 
## selected as the most watched or second most watched sports by a respondent

## most watched sports (V44)
dat_issp_checksports &lt;- dat_issp %&gt;% 
    mutate(sport_relevant_first = case_when(
        country == &quot;Ireland&quot; &amp; V44 == &quot;Other team sports&quot; ~ 1,
        country == &quot;United States&quot; &amp; V44 == &quot;American football&quot; ~ 1,
        country == &quot;Germany&quot; &amp; V44 == &quot;Football, soccer&quot; ~ 1,
        country == &quot;United Kingdom&quot; &amp; V44 == &quot;Football, soccer&quot; ~ 1

    ))

## repeated the same for the second most watched sport (V45)
dat_issp_checksports &lt;- dat_issp_checksports %&gt;% 
    mutate(sport_relevant_second = case_when(
        country == &quot;Ireland&quot; &amp; V45 == &quot;Other team sports&quot; ~ 1,
        country == &quot;United States&quot; &amp; V45 == &quot;American football&quot; ~ 1,
        country == &quot;Germany&quot; &amp; V45 == &quot;Football, soccer&quot; ~ 1,
        country == &quot;United Kingdom&quot; &amp; V45 == &quot;Football, soccer&quot; ~ 1

    ))


#                # create dummy variables that measure whether a sports in a country
## is named as the most watched or second most watched sport
dat_issp_checksports &lt;- dat_issp_checksports %&gt;% 
    mutate(sport_relevant_first = ifelse(is.na(sport_relevant_first), 0, 
                                         sport_relevant_first)) %&gt;%
    mutate(sport_relevant_second = ifelse(is.na(sport_relevant_second), 0, 
                                          sport_relevant_second)) %&gt;% 
    mutate(sport_relevant_1_2 = ifelse(sport_relevant_first == 0,
                                       sport_relevant_second, 
                                       sport_relevant_first))


## rename the sport for each country
dat_issp_checksports &lt;- dat_issp_checksports %&gt;% 
    mutate(sport = case_when(
        country == &quot;United Kingdom&quot; ~ &quot;Football/Soccer&quot;,
        country == &quot;Ireland&quot; ~ &quot;Hurling and Gaelic Football&quot;,
        country == &quot;Germany&quot; ~ &quot;Football/Soccer&quot;,
        country == &quot;United States&quot; ~ &quot;American Football&quot;
    ))


## select only four countries for the plot
dat_issp_firstsecond_select &lt;- dat_issp_checksports %&gt;% 
    filter(country %in% c(&quot;Germany&quot;, &quot;United States&quot;, 
                          &quot;United Kingdom&quot;,
                          &quot;Ireland&quot;)) 

#                # create proportions and 95/90% confidence intervals
## for country and selected sport

set.seed(134)
dat_issp_firstsecond_95 &lt;- dat_issp_firstsecond_select %&gt;% 
    group_by(country, sport) %&gt;% 
    do(data.frame(rbind(Hmisc::smean.cl.boot(.$sport_relevant_1_2,
                                             conf.int = .95)))) %&gt;% 
    mutate(country_sport = paste0(country, &quot;\n&quot;, &quot;(&quot;, sport, &quot;)&quot;))

set.seed(134)
dat_issp_firstsecond_90 &lt;- dat_issp_firstsecond_select %&gt;% 
    group_by(country, sport) %&gt;% 
    do(data.frame(rbind(Hmisc::smean.cl.boot(.$sport_relevant_1_2,
                                             conf.int = .90)))) %&gt;% 
    mutate(country_sport = paste0(country, &quot;\n&quot;, &quot;(&quot;, sport, &quot;)&quot;)) %&gt;%
    ungroup() %&gt;% 
    select(country_sport, Lower_90 = Lower, Upper_90 = Upper)


## merge both datasets for plot
dat_issp_firstsecond &lt;- left_join(dat_issp_firstsecond_95,
                                  dat_issp_firstsecond_90,
                                  by = &quot;country_sport&quot;)

## plot the proportion (displayed as percentages) of most watched/second most watched
## sports
ggplot(dat_issp_firstsecond, 
       aes(x = country_sport, y = Mean,
           ymin = Lower, ymax = Upper)) +
    geom_point(size = 3) +
    geom_linerange(aes(ymin = Lower,
                       ymax = Upper),
                   size = 0.5) +
    geom_linerange(aes(ymin = Lower_90,
                       ymax = Upper_90),
                   size = 1.3) +
    coord_flip() +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1), 
                       limits = c(0, 0.5)) +
    labs(y = &quot;Most frequently watched sports&quot;, x = NULL)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a02.pdf&quot;, width = 9, height = 3)


## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 10:02:57 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
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